The study introduces an integrated framework combining Convolutional Neural Networks (CNNs) and Explainable Artificial Intelligence (XAI) for the enhanced diagnosis of breast cancer using the CBIS-DDSM dataset. Utilizing a fine-tuned ResNet50 architecture, our investigation not only provides effective differentiation of mammographic images into benign and malignant categories but also addresses the opaque "black-box" nature of deep learning models by employing XAI methodologies, namely Grad-CAM, LIME, and SHAP, to interpret CNN decision-making processes for healthcare professionals. Our methodology encompasses an elaborate data preprocessing pipeline and advanced data augmentation techniques to counteract dataset limitations, and transfer learning using pre-trained networks, such as VGG-16, DenseNet and ResNet was employed. A focal point of our study is the evaluation of XAI's effectiveness in interpreting model predictions, highlighted by utilising the Hausdorff measure to assess the alignment between AI-generated explanations and expert annotations quantitatively. This approach plays a critical role for XAI in promoting trustworthiness and ethical fairness in AI-assisted diagnostics. The findings from our research illustrate the effective collaboration between CNNs and XAI in advancing diagnostic methods for breast cancer, thereby facilitating a more seamless integration of advanced AI technologies within clinical settings. By enhancing the interpretability of AI-driven decisions, this work lays the groundwork for improved collaboration between AI systems and medical practitioners, ultimately enriching patient care. Furthermore, the implications of our research extend well beyond the current methodologies, advocating for subsequent inquiries into the integration of multimodal data and the refinement of AI explanations to satisfy the needs of clinical practice.

翻译：本研究提出了一种集成框架，将卷积神经网络（CNN）与可解释人工智能（XAI）相结合，利用CBIS-DDSM数据集实现乳腺癌的增强诊断。采用微调后的ResNet50架构，我们的研究不仅有效区分了乳腺钼靶图像中的良恶性类别，还通过采用Grad-CAM、LIME和SHAP等XAI方法，揭示了深度学习模型不透明的"黑箱"特性，为医疗专业人员解读CNN决策过程提供了依据。我们构建了精细的数据预处理流程和先进的数据增强技术以应对数据集局限性，并应用了基于预训练网络（如VGG-16、DenseNet和ResNet）的迁移学习。研究的核心在于评估XAI在解释模型预测中的有效性，特别采用Hausdorff度量对AI生成解释与专家标注之间的一致性进行定量评估。该方法在XAI促进AI辅助诊断可信度与伦理公平性方面发挥着关键作用。研究结果表明，CNN与XAI的协同作用可有效提升乳腺癌诊断方法，从而推动先进AI技术在临床环境中的无缝整合。通过增强AI驱动决策的可解释性，本研究为AI系统与医疗从业者深化协作奠定了基础，最终改善患者护理。此外，本研究的启示远超当前方法论范畴，倡导后续探索多模态数据整合及优化AI解释以满足临床实践需求。